An open-source machine learning framework for global analyses of parton distributions
High Energy Physics - Phenomenology
2021-09-08 v1
Abstract
We present the software framework underlying the NNPDF4.0 global determination of parton distribution functions (PDFs). The code is released under an open source licence and is accompanied by extensive documentation and examples. The code base is composed by a PDF fitting package, tools to handle experimental data and to efficiently compare it to theoretical predictions, and a versatile analysis framework. In addition to ensuring the reproducibility of the NNPDF4.0 (and subsequent) determination, the public release of the NNPDF fitting framework enables a number of phenomenological applications and the production of PDF fits under user-defined data and theory assumptions.
Keywords
Cite
@article{arxiv.2109.02671,
title = {An open-source machine learning framework for global analyses of parton distributions},
author = {Richard D. Ball and Stefano Carrazza and Juan Cruz-Martinez and Luigi Del Debbio and Stefano Forte and Tommaso Giani and Shayan Iranipour and Zahari Kassabov and Jose I. Latorre and Emanuele R. Nocera and Rosalyn L. Pearson and Juan Rojo and Roy Stegeman and Christopher Schwan and Maria Ubiali and Cameron Voisey and Michael Wilson},
journal= {arXiv preprint arXiv:2109.02671},
year = {2021}
}
Comments
14 pages, 4 figures. A companion paper describes the NNPDF4.0 PDFs